DOI

10.5703/1288284313299

Abstract

For the past two decades, weigh-in-motion (WIM) sensors have been used in the United States to collect vehicle weight data for designing pavements and monitoring their performance. The use of these sensors is now being expanded for enforcement purposes to provide virtual weigh stations for screening vehicles in traffic streams for overweight violations. A study found that static weigh stations in Indiana were effective for identifying safety violations, but ineffective for identifying overweight vehicles. It was also determined that the alternative approach to identifying overweight vehicles using virtual weigh stations requires a high level of WIM data accuracy and reliability that can only be attained with a rigorous quality control program. Accurate WIM data is also essential to the success of the Long-term Pavement Performance project and the development of new pavement design methods. This report proposes a quality control program that addresses vehicle classification, speed, axle spacing, and weight accuracy by identifying robust metrics that can be continuously monitored using statistical process control procedures that differentiate between sensor noise and events that require intervention. The speed and axle spacing accuracy is assessed by examining the drive tandem axle spacing of the population of Class 9 vehicles. The weight accuracy is assessed by examining the left-right steer axle residual weight of the population of Class 9 vehicles. Data mining of these metrics revealed variations in the data caused by incorrect calibration, sensor failure, temperature, and precipitation. The accuracy metrics could be implemented in a performance-based specification for WIM systems that is more feasible to enforce than the current specifications based on comparing static vehicle weights with dynamic vehicle weights measured by the WIM sensors. The quality control program can also be used by agencies to prioritize maintenance to more effectively allocate the limited funds available for sensor repair and calibration. This research provides a tool that agencies can use to obtain and sustain higher quality WIM data.